Сравнение на методи

	Модел ARIMA (Autoregressive Integrated Moving Average)	Байесов регресионен модел	Анализ на прекъснати времеви редове (ITS)	Марковски Монте Карло вериги (MCMC)
Област	Иконометрия	Бейсови методи	Причинно-следствено заключение	Бейсови методи
Семейство	Regression model	Bayesian methods	Regression model	Bayesian methods
Година на възникване	2015	—	2002	—
Създател	Box & Jenkins (Box-Jenkins methodology)	—	Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial)	—
Тип	Univariate time-series model	Bayesian linear model	Quasi-experimental segmented regression	Posterior sampling algorithm
Основополагащ източник	Box, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021	Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955	Bernal, J. L., Cummins, S., & Gasparrini, A. (2017). Interrupted time series regression for the evaluation of public health interventions: a tutorial. International Journal of Epidemiology, 46(1), 348-355.	Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955
Други названия	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	bayesian linear regression, probabilistic regression, bayesian regresyon	ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi	markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo)
Свързани	5	2	5	3
Резюме	ARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015).	Bayesian regression is a probabilistic version of linear regression that treats the model parameters as uncertain quantities. Instead of returning a single best-fit estimate, it combines prior knowledge with the observed data to produce a full posterior probability distribution for each parameter, from which credible intervals and predictions are read off.	Interrupted Time Series analysis is a quasi-experimental design that estimates the effect of a single, well-dated intervention by comparing the trajectory of an outcome before and after it occurs. Formalised as segmented regression by Wagner and colleagues (2002) and popularised as a public-health evaluation tutorial by Bernal, Cummins and Gasparrini (2017), it separates the intervention's impact into a change in level and a change in slope.	Markov Chain Monte Carlo (MCMC) is a family of computational algorithms for sampling from complex probability distributions, most commonly the posterior distributions that arise in Bayesian inference. Rather than computing posteriors analytically — which is rarely possible for realistic models — MCMC constructs a Markov chain whose stationary distribution is the target posterior and draws dependent samples from it, enabling full probabilistic inference for virtually any model.